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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.08371 |
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| _version_ | 1866913233598676992 |
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| author | Esteban, A. Zafra, A. Romero, C. |
| author_facet | Esteban, A. Zafra, A. Romero, C. |
| contents | The wide availability of specific courses together with the flexibility of academic plans in university studies reveal the importance of Recommendation Systems (RSs) in this area. These systems appear as tools that help students to choose courses that suit to their personal interests and their academic performance. This paper presents a hybrid RS that combines Collaborative Filtering (CF) and Content-based Filtering (CBF) using multiple criteria related both to student and course information to recommend the most suitable courses to the students. A Genetic Algorithm (GA) has been developed to automatically discover the optimal RS configuration which include both the most relevant criteria and the configuration of the rest of parameters. The experimental study has used real information of Computer Science Degree of University of Cordoba (Spain) including information gathered from students during three academic years, counting on 2500 entries of 95 students and 63 courses. Experimental results show a study of the most relevant criteria for the course recommendation, the importance of using a hybrid model that combines both student information and course information to increase the reliability of the recommendations as well as an excellent performance compared to previous models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08371 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization Esteban, A. Zafra, A. Romero, C. Machine Learning The wide availability of specific courses together with the flexibility of academic plans in university studies reveal the importance of Recommendation Systems (RSs) in this area. These systems appear as tools that help students to choose courses that suit to their personal interests and their academic performance. This paper presents a hybrid RS that combines Collaborative Filtering (CF) and Content-based Filtering (CBF) using multiple criteria related both to student and course information to recommend the most suitable courses to the students. A Genetic Algorithm (GA) has been developed to automatically discover the optimal RS configuration which include both the most relevant criteria and the configuration of the rest of parameters. The experimental study has used real information of Computer Science Degree of University of Cordoba (Spain) including information gathered from students during three academic years, counting on 2500 entries of 95 students and 63 courses. Experimental results show a study of the most relevant criteria for the course recommendation, the importance of using a hybrid model that combines both student information and course information to increase the reliability of the recommendations as well as an excellent performance compared to previous models. |
| title | Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.08371 |